第一个是通过competition，第二个是直接进入到data sets这个界面进行寻找。那么这两个的入口我们都可以在Kaggle的网站首页上直接找到这如图上所示的那样。那么competition它是Kaggle上就是说会有很多实时进行的竞赛，他们一般都会要求你用了ML或者AI的model去实现一个目标，那么很多也会提供一定的奖金。感兴趣的同学可以就...
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# Store target variable of training data in a safe place survived_train = df_train.Survived # Concatenate training and test sets data = pd.concat([df_train.drop(['Survived'], axis=1), df_test]) 使用info()方法签出新的DataFrame数据。 data.info() <class 'pandas.core.frame.DataFrame'> ...
fig=plt.figure()fig.set(alpha=0.2)# 设定图表颜色alpha参数plt.subplot2grid((2,3),(0,0))# 在一张大图里分列几个小图data_train.Survived.value_counts().plot(kind='bar')# 柱状图 plt.title(u"获救情况 (1为获救)")# 标题plt.ylabel(u"人数")plt.subplot2grid((2,3),(0,1))data_train....
gbm=lgb.train(params,train_data,valid_sets=[validation_data])# 模型预测 y_pred=gbm.predict(X_test)y_pred=[list(x).index(max(x))forxiny_pred]print(y_pred)# 模型评估print(accuracy_score(y_test,y_pred)) (2)基于Scikit-learn接口的分类 ...
datadf_train=pd.read_csv('data/train.csv')df_test=pd.read_csv('data/test.csv')# Store target variable of training data in a safe placesurvived_train=df_train.Survived# Concatenate training and test setsdata=pd.concat([df_train.drop(['Survived'],axis=1),df_test])# View headdata....
from__future__importdivision, print_function, absolute_import# Import MNIST datafromtensorflow.examples.tutorials.mnistimportinput_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)importtensorflowastfimportmatplotlib.pyplotaspltimportnumpyasnp# Training Parameterslearning_rate =0.001...
This book is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this book useful. A basic understanding of machine learning concepts will help you make ...
Such models learn from labelled data, which is data that includes whether a passenger survived (called "model training"), and then predict on unlabelled data. On Kaggle, a platform for predictive modelling and analytics competitions, these are called train and test sets because You want to ...
Afterwards, you merge the train and test data sets (with exception of the 'Survived' column of df_train) and store the result in data. Remember that you do this because you want to make sure that any preprocessing that you do on the data is reflected in both the train and test sets!